Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

Systematic approaches to machine learning models for predicting pesticide toxicity

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

In silico environmental risk assessment improves efficiency for pesticide safety management

Pesticides are indispensable to maintain crop quality and food production worldwide, but their use also poses environmental risks. Pesticide risk assessment involves a series of complex, expensive and time-consuming toxicity tests. To improve the efficiency and accuracy for assessing the environmental impact of pesticides, numerous computational tools have been developed. However, there is a notable deficiency in critical analysis or a systematic summary of environmental risk assessment tools and their applicable contexts. Here, many of the current approaches and tools for assessing environmental risks posed by pesticides are reviewed, and the question of whether these tools are fit for use on complex multicomponent scenarios is discussed. We analyze the adaptations of these tools to aquatic and terrestrial ecosystems, followed by the provision of resources for predicting pesticide concentrations in environmental medias, including air, soil and water. The successful application of computational tools for risk assessment and interpretation of predicted results will also be discussed. This assessment serves as a valuable resource, enabling scientists to utilize suitable models to enhance the robustness of pesticides risk assessments.

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks

Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure-activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related tasks─each predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi-task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q²_LOO = 0.620-0.663) and externally (Q²F_n = 0.693-0.868, CCC_ext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO₂, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

A Simple Approach to the Toxicity Prediction of Anilines and Phenols Towards Aquatic Organisms

Chemicals pollution in the environment has attracted attention all over the world, and the toxicity prediction of chemical pollutants has become quite important. In this paper, we introduce a simple approach to predict the toxicity of some chemical components, in which the Tchebichef image moment (TM) method was employed to extract useful chemical information from the images of molecular structures to establish quantitative structure-activity relationship (QSAR) prediction models. The proposed approach was applied to predict the toxicity of anilines and phenols for the aquatic organisms of P. subcapitata and V. fischeri, in which the obtained TMs were defined as the independent variables, while the biological toxicity (pEC₅₀) was regarded to be the dependent variable. Then, the predictive models were established by stepwise regression, respectively. The obtained squared correlation coefficients of leave-one-out cross-validation (Q²) for training sets and the predictive squared correlation coefficients (R_p²) for test sets of the two groups of data were higher than 0.79 and 0.75, respectively, which indicated that the obtained models possessed satisfactory accuracy and reliability. Compared with several reported methods, the proposed approach was more convenient and has a higher predictive capability. Our study provides another perspective in QSAR research.

Design of potential anti-tumor PARP-1 inhibitors by QSAR and molecular modeling studies

Poly ADP-ribose polymerase-1 (PARP-1) inhibitors have been recognized as new agents for the treatment of patients with breast cancer type 1 (BRCA1) disorders. The quantitative structure-activity relationships (QSAR) technique was used in order to achieve the required medicines for anticancer activity easier and faster. In this study, the QSAR method was developed to predict the half-maximal inhibitory concentration (IC₅₀) of 51 1H-benzo[d]immidazole-4-carboxamide derivatives by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods. Results in the best QSAR model represented the coefficient of leave-one-out cross-validation (Q _cv ² ) = 0.971, correlation coefficient (R²) = 0.977, Fisher parameter (F) = 259.016 and root mean square error (RMSE) = 0.095, respectively, which indicated that the LS-SVM model had a good potential to predict the pIC₅₀ (9 - log(IC₅₀ nM)) values compared with other modeling methods. Also, molecular docking evaluated interactions between ligands and enzyme and their free energy of binding were calculated and used as descriptors. Molecular docking and the QSAR study completed each other. The results represented that the final model can be useful to design some new inhibitors. So, the knowledge of the QSAR modeling and molecular docking was used in pIC₅₀ prediction and 51 new compounds were developed as PARP-1 inhibitors that 9 compounds had the best-proposed values for pIC₅₀. The maximum enhancement of the inhibitory activity of compounds was 33.394%.

The activity prediction of indole inhibitors against HCV NS5B polymerase

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC₅₀ ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q² ) for the training set and the R-squared of prediction ( R p 2 ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

Chemometric modeling of Daphnia magna toxicity of agrochemicals

Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.

In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

Aquatic toxicity is a crucial endpoint for evaluating chemically adverse effects on ecosystems. Therefore, we developed in silico methods for the prediction of chemical aquatic toxicity in marine environment. At first, a diverse data set including different crustacean species was constructed. We then built local binary models using Mysidae data and global binary models using Mysidae, Palaemonidae, and Penaeidae data. Molecular fingerprints and descriptors were employed to represent chemical structures separately. All the models were built by six machine learning methods. The AUC (area under the receiver operating characteristic curve) values of the better local and global models were around 0.8 and 0.9 for the test sets, respectively. We also identified several chemicals with selective toxicity on different species. The analysis of selective toxicity would promote to design greener chemicals in a specific environment. Finally, to understand and interpret the models, we explored the relationships between chemical aquatic toxicity and the molecular descriptors. Our study would be helpful in gaining further insights into marine organisms, prediction of chemical aquatic toxicity and prioritization of environmental hazard assessment.

In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods

With industrial development and eventual commercial use, environmental chemicals through accidental spills and effluents appear more frequently in aquatic ecosystems and may produce an enormous effect on water, soil, wildlife and human health. Therefore, aquatic toxicity becomes an increasingly important endpoint in the evaluation of the environmental impact of chemicals. In this study, based on ECOTOX database, a large data set containing 824 diverse compounds with experimental 48 h EC50 values on crustaceans was compiled. A series of in silico models were then developed using six machine learning methods combined with seven types of molecular fingerprints. Performance of these models was measured by an external validation set, involving 246 molecules. The best model proposed is MACCS fingerprint and SVM algorithm with high accuracy of 0.87 for external validation set. Additionally, we proposed five structural alerts identified by information gain and substructure frequency analysis for mechanistic interpretation. The models and structural alerts can provide critical information and useful tools for a priori evaluation of chemical aquatic toxicity in environmental hazard assessment.

Transchem project - Part I: Impact of long-term exposure to pendimethalin on the health status of rainbow trout (Oncorhynchus mykiss L.) genitors

Pendimethalin is a herbicide active substance commonly used in terrestrial agricultural systems and is thus detected at high concentrations in the surface water of several European countries. Previous studies reported several histopathological changes, enzymatic antioxidant modulation and immunity disturbance in fish exposed to this pesticide. The objective of this work was to investigate the direct effects of long-term exposure to environmental concentrations of pendimethalin over a period of 18 months in rainbow trout (Oncorhynchus mykiss) genitors. To do so, an experimental system consisting of eight similar 400 L tanks with a flow-through of fresh river water was used to perform daily chemical contamination. Fish were exposed to 850 ng/L for one hour and the pendimethalin concentration was then gradually diluted during the day to maintain optimal conditions for the fish throughout the experiment and to achieve a mean theoretical exposure level of around 100 ng L^-1 per day. Every November, males and females were stripped to collect eggs and sperm and two new first generations of offspring were obtained. Kinetic sampling revealed differences in immune system parameters and antioxidative defences in the contaminated trout compared to the controls, due to pesticide exposure combined with seasonal changes related to gamete maturation. Moreover, reproductive capacity was significantly affected by exposure to the herbicide; a time lag of more than five weeks was observed for egg maturation in contaminated females and high bioconcentrations of pendimethalin were measured in eggs and sperm. Chemical transfer from genitors to offspring via gametes may affect embryo development and negatively impact the early stages of development.

In silico prediction of pesticide aquatic toxicity with chemical category approaches

Aquatic toxicity is an important issue in pesticide development. In this study, using nine molecular fingerprints to describe pesticides, binary and ternary classification models were constructed to predict aquatic toxicity of pesticides via six machine learning methods: Naïve Bayes (NB), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Classification Tree (CT), Random Forest (RF) and Support Vector Machine (SVM). For the binary models, local models were obtained with 829 pesticides on rainbow trout (RT) and 151 pesticides on lepomis (LP), and global models were constructed on the basis of 1258 diverse pesticides on RT and LP and 278 on other fish species. After analyzing the local binary models, we found that fish species caused influence in terms of accuracy. Considering the data size and predictive range, the 1258 pesticides were also used to build global ternary models. The best local binary models were Maccs_ANN for RT and Maccs_SVM for LP, which exhibited accuracies of 0.90 and 0.90, respectively. For global binary models, the best model was Graph_SVM with an accuracy of 0.89. Accuracy of the best global ternary model Graph_SVM was 0.81, which was a little lower than that of the best global binary model. In addition, several substructural alerts were identified including nitrobenzene, chloroalkene and nitrile, which could significantly correlate with pesticide aquatic toxicity. This study provides a useful tool for an early evaluation of pesticide aquatic toxicity in environmental risk assessment.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals

The authors modeled the 72-h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action-based local quantitative structure-toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co-operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high-production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk-management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012-1019. © 2016 SETAC.

Multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of nano-metal oxides

The metal oxide nanoparticles (MeONPs) due to their unique physico-chemical properties have widely been used in different products. Current studies have established toxicity of some NPs to human and environment, hence, imply for their comprehensive safety assessment. Here, the potential of using a multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of various MeONPs has been investigated. A multi-target QSTR model has been established using four different experimental toxicity data sets of MeONPs. Diversity of the considered experimental toxicity data sets was tested using the Kruskal-Wallis (K-W) statistics. The optimal validated model yielded high correlations (R² between 0.828 and 0.956) between the experimental and simultaneously predicted endpoint toxicity values in test arrays for all the four systems. The structural features (oxygen percent, LogS, and Mulliken's electronegativity) identified by the QSTR model were mechanistically interpretable in view of the accepted toxicity mechanisms for NPs. Single target QSTR models were also established (R²_Test >0.882) for individual toxicity endpoint prediction of MeONPs. The performance of the multi-target QSTR model was closely comparable with individual models and with those reported earlier in the literature for toxicity prediction of NPs. The model reliably predicts the toxicity of all considered MeONPs, and the methodology is expected to provide guidance for the future design of safe NP-based products. The proposed multi-target QSTR can be successfully used for screening new, untested metal oxide NPs for their safety assessment within the defined applicability domain of the model.

Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches

The persistence and the removal of organic chemicals from the atmosphere are largely determined by their reactions with the OH radical and O3. Experimental determinations of the kinetic rate constants of OH and O3 with a large number of chemicals are tedious and resource intensive and development of computational approaches has widely been advocated. Recently, ensemble machine learning (EML) methods have emerged as unbiased tools to establish relationship between independent and dependent variables having a nonlinear dependence. In this study, EML-based, temperature-dependent quantitative structure-reactivity relationship (QSRR) models have been developed for predicting the kinetic rate constants for OH (kOH) and O3 (kO3) reactions with diverse chemicals. Structural diversity of chemicals was evaluated using a Tanimoto similarity index. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation performed employing statistical checks. In test data, the EML QSRR models yielded correlation (R (2)) of ≥0.91 between the measured and the predicted reactivities. The applicability domains of the constructed models were determined using methods based on descriptors range, Euclidean distance, leverage, and standardization approaches. The prediction accuracies for the higher reactivity compounds were relatively better than those of the low reactivity compounds. Proposed EML QSRR models performed well and outperformed the previous reports. The proposed QSRR models can make predictions of rate constants at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards OH radical and O3 in the atmosphere.

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes

The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. The generalization and prediction abilities of the constructed QSAR models were evaluated by internal and external validation procedures and by deriving several stringent statistical criteria parameters. In the test set, the two models (DTF and DTB) yielded R2 of 0.856 and 0.945, between the experimental and predicted endpoint toxicity values. The models were also evaluated for predictive use through the most recent criteria based on root mean squared error (RMSE) and mean absolute error (MAE). The values of various statistical validation coefficients derived for the test data were above their respective threshold limits and thus put a high confidence in this analysis. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals and can be useful tools for screening new chemicals for safety assessment.

In silico prediction of the developmental toxicity of diverse organic chemicals in rodents for regulatory purposes

The experimental determination of the developmental toxicity potential (LEL) of chemicals is not only tedious, time and resource intensive, but it also involves unethical tests on animals. In this study, we have established quantitative structure activity relationship (QSAR) models for predicting the developmental toxicity potential of chemicals in rodents following the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) based local (L-QSAR), global (G-QSAR) and interspecies quantitative structure activity-activity relationship (ISC QSAAR) models were developed for estimating the LEL (lowest effective level) dose of chemicals for developmental toxicity in rats and rabbits. The structural features of chemicals responsible for developmental toxicity in rodents were extracted and used in QSAR/QSAAR analysis. The external predictive power of the developed models was evaluated through the internal and external validation procedures. In test data, the L-QSAR models (DTF, DTB) yielded R2 values of >0.846 (rat) and >0.906 (rabbit), whereas in G-QSAR, the correlation value was >0.870 between the measured and predicted endpoint values. In ISC QSAAR models, the R2 values in test data were 0.830 (DTF) and 0.927 (DTB), respectively. Values of various statistical validation coefficients derived from the test data (except rm2 in DTF based rat L-QSAR and ISC QSAAR models) were above their respective threshold limits, thus putting a high confidence in this analysis. The prediction quality of the developed QSAR/QSAAR models was also assessed using the mean absolute error (MAE) criteria and found good. The applicability domains of the constructed models were defined using the descriptor range, leverage, and standardization approaches. The results suggest that the developed QSAR/QSAAR models can reliably predict the developmental toxicity potential of structurally diverse chemicals in rodents, generating useful toxicity data for risk assessment in humans.

Modeling the reactivity of ozone and sulphate radicals towards organic chemicals in water using machine learning approaches

QSRR modeling and correlative distribution of measured and model predicted values of rate constants (k_O3andkS_O4) of reactions of O₃and SO₄˙⁻radicals with diverse organic chemicals in aqueous medium.

Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches

The prediction of the plasma protein binding (PPB) affinity of chemicals is of paramount significance in the drug development process. In this study, ensemble machine learning-based QSPR models have been established for a four-category classification and PPB affinity prediction of diverse compounds using a large PPB dataset of 930 compounds and in accordance with the OECD guidelines. The structural diversity of the chemicals was tested by the Tanimoto similarity index. The external predictive power of the developed QSPR models was evaluated through internal and external validations. In the QSPR models, XLogP was the most important descriptor. In the test data, the classification QSPR models rendered an accuracy of >93%, while the regression QSPR models yielded r(2) of >0.920 between the measured and predicted PPB affinities, with the root mean squared error <9.77. Values of statistical coefficients derived for the test data were above their threshold limits, thus put a high confidence in this analysis. The QSPR models in this study performed better than any of the previous studies. The results suggest that the developed QSPR models are reliable for predicting the PPB affinity of structurally diverse chemicals. They can be useful for initial screening of candidate molecules in the drug development process.

Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches

The safety assessment processes require the toxicity data of chemicals in multiple test species and thus, emphasize the need for computational methods capable of toxicity prediction in multiple test species. Pesticides are designed toxic substances and find extensive applications worldwide. In this study, we have established local and global QSTR (quantitative structure-toxicity relationship) and ISC QSAAR (interspecies correlation quantitative structure activity-activity relationship) models for predicting the toxicities of pesticides in multiple aquatic test species using the toxicity data in crustacean (Daphnia magna, Americamysis bahia, Gammarus fasciatus, and Penaeus duorarum) and fish (Oncorhynchus mykiss and Lepomis macrochirus) species in accordance with the OECD guidelines. The ensemble learning based QSTR models (decision tree forest, DTF and decision tree boost, DTB) were constructed and validated using several statistical coefficients derived on the test data. In all the QSTR and QSAAR models, Log P was an important predictor. The constructed local, global and interspecies QSAAR models yielded high correlations (R2) of >0.941; >0.943 and >0.826, respectively between the measured and model predicted endpoint toxicity values in the test data. The performances of the local and global QSTR models were comparable. Furthermore, the chemical applicability domains of these QSTR/QSAAR models were determined using the leverage and standardization approaches. The results suggest for the appropriateness of the developed QSTR/QSAAR models to reliably predict the aquatic toxicity of structurally diverse pesticides in multiple test species and can be used for the screening and prioritization of new pesticides.

Inter-moieties reactivity correlations: an approach to estimate the reactivity endpoints of major atmospheric reactants towards organic chemicals

The figure shows the DTB based IMRC QRRR modelling and predicted values of the rate constants (log k_OH, log k_O3).

Predicting aquatic toxicities of benzene derivatives in multiple test species using local, global and interspecies QSTR modeling approaches

A flow diagram showing QSTR modeling strategy for aquatic toxicity prediction of benzene derivatives in multiple test species.